Remote sensor and telemetry, nowadays represent an exceptional tool for monitoring and maintaining critical systems especially in aerospace engineering. Spacecrafts, rovers and other aerospace devices always depend on fuel cells. Health and reliability of the missions are very crucial. This paper offers an in-depth report on fuel cell health anomaly detection methods that are carried out on dataset taken from NASA. Our approach involves using several machine learning and statistical methods to derive the unexpected patterns out of the data. In essence our report shows that they are effective in achieving the desired goal, and can be used in a practical setting. The paper provides the development of fuel cell health monitoring for successful mission in space exploration and improves mission reliability. This paper presents interesting results that the aerospace industry and the general anomaly detection fields may find useful.


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    Title :

    Advanced Anomaly Detection For Fuel Cell Health Monitoring


    Contributors:


    Publication date :

    2024-03-01


    Size :

    2431229 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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